Table of Contents
General Information
This workshop introduces participants to open source tools for geospatial and temporal analysis of vector and raster data. The workshop will emphasize R packages and, to a lesser extent, Python libraries commonly used in GIS. Through lectures and hands-on computer labs, listed in the schedule below, SESYNC staff will aim to accelerate your adoption of computational resources for all phases of data-driven geospatial research.
Participants should expect to:
- learn new scientific computing skills
- overcome specific or conceptual project hurdles
- gain coding confidence
- have fun
Instructors:
- Benoit Parmentier, Data Scientist
- Ian Carroll, Data Scientist
- Rachael Blake, Data Scientist
- Kelly Hondula, Quantitative Researcher and Computer Programmer
- Elizabeth Green, Computational Research Assistant
When:
Wednesday, March 27, 2019 to Friday, March 29, 2019
Where:
1 Park Place, Suite 300
Annapolis, MD 21401
Get directions with OpenStreetMap or Google Maps.
Contact:
Please email with any questions, including installation issues, or for information not covered here.
Requirements
- Participants must bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.).
- After the course, participants must complete a reimbursement form to recover allowed travel expenses.
Schedule
Sessions begin promptly at 9:00 am.
Nourishment will arrive at the 10:30 am coffee break, the on-site lunch provided by SESYNC at 12:30 pm, and an afternoon break. Trainees are responsible for their own breakfast and dinner arrangements (we can make recommendations).
Software
Maintaining a functioning, up-to-date software environment is a big challenge! SESYNC provides a cloud platform capable of supporting the software needs for the short course, so there is nothing for you to install in advance. During or after the course, you may choose to install the software listed below—it is all free and open source—on your own machines. Consider the list a work-in-progress; we appreciate your suggestions for surmounting installation difficulties. An alternative to the list below is the Anaconda R/Python Distribution, the big-box store of data science.
For each item, you’ll find a link to a page with installation instructions,
where available, or else to the downloadable installer. Windows users have
little alternative to maintaining each software independently. MacOS users are
encouraged to use Homebrew–the missing package manager for OS X–via the
Terminal, for which we provide the relevant brew install <PACKAGENAME> command. A Linux terminal command that might work on Ubuntu is apt-get install <PACKAGENAME> but YMMV.
git
| Windows/macOS Installer | https://git-scm.com/downloads |
| macOS package manager | brew install git |
| Linux package manager | apt-get install git |
R
| Windows/macOS Installer | https://cran.rstudio.com/ |
| macOS package manager | brew cask install r-app or brew install r (advanced) |
| Linux package manager | apt-get install r-base |
RStudio (free version)
| Windows/macOS Installer | https://www.rstudio.com/products/rstudio/download2/ |
| macOS package manager | brew cask install rstudio |
| Linux package manager |
Python 3.x
| Windows/macOS Installer | https://www.python.org/downloads/ |
| macOS package manager | brew install python |
| Linux package manager | apt-get install python3 |
PostgreSQL
| Windows/macOS Installer | https://www.postgresql.org/download/ |
| macOS package manager | brew install postgresql |
| Linux package manager | apt-get install postgresql |
postGIS
| Windows/ |
https://postgis.net/install/ |
| macOS package manager | brew install postgis |
| Linux package manager with ubuntugis | apt-get install postgis |
R Packages
Install the following R packages after R and Rstudio are installed. Open RStudio
and, for each package below, type install.packages(<PACKAGENAME>) at the prompt
and press return. For information on any package, navigate to
http://cran.r-project.org/package=<PACKAGENAME>. Bold packages are red hot.
| tidyr | forecast | readr | ROCR |
| dplyr | gstat | modules | rgeos |
| leaflet | plyr | rmarkdown | RPostgreSQL |
| stringr | lubridate | randomForest | sf |
| ggplot2 | mapview | raster | shiny |
| data.table | dbplyr | rasterVis | sphet |
| lme4 | colorRamps | rgdal | spdep |
| xts | zoo | network | caret |
| magick | sp |
Python Packages
The following Python packages need to be installed Python. Open a shell/terminal
and, for each package below, run pip3 install <PACKAGENAME>. Bold packages are flying off the shelves!
| geopandas | requests | sqlalchemy | pydap |
| jupyterlab | numpy | pysal | rasterio |
| beautifulsoup4 | pygresql | pandas | |
| requests | lxml | matplotlib |
After installing jupyterlab, run jupyter serverextension enable --py jupyterlab
--sys-prefix in the shell/terminal to complete installation.
JupyterLab runs through
your browser, to launch it, enter jupyter lab in the shell/terminal, and stop
it with Ctrl-C.
Acknowledgments
Portions of the instructional materials are adopted from Data Carpentry and Software Carpentry. The structure of the curriculum as well as the teaching style are informed by Software Carpentry.
